Various systems for determining user engagement are available to advertisers. These systems usually track user engagement by collecting data on users' interactions with media assets. The collected data is analyzed, and a degree of engagement for each user is calculated. The degrees of engagement are aggregated and a general user engagement level is determined. For example, if one hundred users watched a movie, and, of those users, forty fast-forwarded a portion of the movie, while another forty changed a channel away from the movie and then back to the movie, the user engagement score may not be very positive for that movie. In contrast, if eighty out of one hundred users watched the movie from start to finish, twenty watched certain portions multiple times, and some of the users posted positive comments about the movie on social media, the movie may get a very positive score.
This approach has various shortcomings. For example, this approach does not account for differences in behavior between various users. For example, some users habitually change channels multiple times when consuming media, while others do not. Thus, the approach taken by current systems does not account for those differences. In another example, some users historically post their comments on social media about movies they watch, while others usually do not. Thus, a user posting a comment about a movie does not indicate that the user is engaged with the movie more than with any other movie. In contrast, not posting a comment about a movie may not be an indication that a particular user is not engaged with the movie.